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Machine Learning Engineer II

Atlanta, GA, US • Posted 17 hours ago • Updated 5 hours ago

Full Time

On-site

The Coca-Cola Company

Fitment

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Job Details

Skills

Accountability
People Management
Machine Learning (ML)
Real-time
Use Cases
Usability
IT Management
Technical Direction
Design Review
Mentorship
Coaching
Management
Storage
Scalability
Encryption
Enterprise Architecture
Business Strategy
Microsoft Azure
Partnership
Communication
Regulatory Compliance
Collaboration
Privacy
Legal
Customer Engagement
Leadership
Clarity
Data Quality
Computer Science
Data Science
Analytics
Data Modeling
Cloud Computing

Accountability
People Management
Machine Learning (ML)
Real-time
Use Cases
Usability
IT Management
Technical Direction
Design Review
Mentorship
Coaching
Management
Storage
Scalability
Encryption
Enterprise Architecture
Business Strategy
Microsoft Azure
Partnership
Communication
Regulatory Compliance
Collaboration
Privacy
Legal
Customer Engagement
Leadership
Clarity
Data Quality
Computer Science
Data Science
Analytics
Data Modeling
Cloud Computing

Summary

The Senior Manager, Software Engineer, Data Platform & Segmentation is a senior individual contributor accountable for the technical vision, design, and evolution of data platforms and segmentation capabilities that power Customer and Commercial product teams operating under a modern Product Operating Model.

This role functions as a hands-on technical leader and multiplier, shaping how customer, commercial, and behavioral data is modeled, segmented, and activated across products, enabling better decisions, personalization, and measurable business outcomes.

The role emphasizes deep technical expertise, product partnership, and architectural leadership, rather than people management.

Core Accountabilities

Product Model & Discovery Partnership

Partner closely with Product Managers, Designers, and Tech Leads to co-own outcomes, not just data assets.
Participate actively in product discovery to ensure segmentation strategies are technically feasible, scalable, and analytically sound.
Translate business and customer questions into durable data models and segmentation frameworks.

Machine Learning

Strong experience in Machine Learning engineering, leveraging ML models to build Segmentation strategies

Experience operationalizing ML-driven segmentation, including integrating segmentation outputs into other products
Collaborating with data scientists
managing standards for model life cycle, monitoring drift, retraining, etc.
Understanding of Azure ML (or any other cloud) workspaces and integrating them with Azure pipelines

Data Platform & Segmentation Architecture

Define and evolve the segmentation architecture across customer and commercial data domains.
Design scalable data models that support real-time, near real time, and batch segmentation use cases.
Ensure segmentation logic is reusable, explainable, and consistent across channels and products.
Make explicit trade-offs across latency, accuracy, cost, privacy, and maintainability.

Engineering Execution & Data Quality

Build and maintain high-quality, production-grade data pipelines and services.
Ensure strong standards for data quality, lineage, observability, and reliability.
Reduce fragmentation and duplication in segmentation logic across teams.
Leverage metrics to continuously improve data freshness, accuracy, and usability.

Individual Contributor Technical Leadership

Act as a go-to expert for data platform and segmentation design.
Lead complex technical initiatives end-to-end through hands-on contribution.
Influence technical direction through design reviews, reference implementations, and documented standards.
Mentor senior engineers and Tech Leads through coaching and technical guidance (without direct management responsibility).

Microsoft Azure Data Platform & Fabric Expertise

Demonstrate deep, hands-on expertise with Microsoft Azure data services and their application in large-scale, product-centric environments.
Design and evolve segmentation and data platform architectures leveraging Azure Data Fabric concepts, ensuring interoperability, governance, and reuse across domains.
Apply strong architectural judgment across core Azure data products, including data ingestion, storage, processing, analytics, and activation layers.
Optimize designs across cost, performance, latency, and scalability, using Azure-native capabilities and patterns.
Ensure secure-by-design implementations aligned with Azure identity, access, encryption, and compliance controls.
Partner with enterprise architecture, cloud, and security teams to ensure Azure data platform decisions align with broader enterprise strategy while preserving team autonomy.
Stay current on Azure data platform evolution and proactively assess new capabilities for business value, not novelty.

Business Partnership & Communication

Serve as a trusted technical partner to Customer and Commercial stakeholders.
Communicate segmentation concepts, assumptions, and limitations in clear business language.
Proactively surface data constraints, privacy considerations, and trade-offs to enable informed decisions.
Support external partner and vendor conversations as a technical authority when needed.

Governance, Privacy & Compliance

Ensure segmentation approaches comply with data privacy, consent, and regulatory requirements.
Collaborate with Security, Privacy, and Legal teams to embed governance into platform design-not bolt it on later.
Advocate for responsible and ethical use of customer and commercial data.

Success Measures

Segmentation capabilities measurably improve customer engagement and commercial outcomes.
Reduced duplication and inconsistency in segmentation logic across products.
Improved data quality, freshness, and trustworthiness.
Faster time-to-insight and activation for product teams.
Platforms and models that scale with growth while controlling cost and risk.

Leadership Profile

Outcome-driven, not data for data's sake
Deep technical expertise with strong product intuition
Influences through credibility and clarity, not authority
Comfortable operating in ambiguity and evolving problem spaces
Holds a high bar for data quality, ethics, and reliability

Required Experience & Capabilities

Bachelor's degree in Computer Science, Engineering, Data Science, or equivalent experience.
8+ years of hands-on experience in data platform, analytics engineering, or backend engineering roles.
Deep expertise in data modeling, segmentation strategies, and large-scale data systems.
Strong experience with cloud-native data platforms and modern data tooling.
Proven ability to partner closely with product and business stakeholders.
Demonstrated impact as a senior individual contributor on complex, cross-team initiatives.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: coke
Position Id: R-134645
Posted 17 hours ago

Company Info

About The Coca-Cola Company

On May 8, 1886, Dr. John Pemberton brought his perfected syrup to Jacobs' Pharmacy in downtown Atlanta, where the first glass of Coca‑Cola was poured. In its first year, about nine Coca-Cola drinks were served per day.

Today, The Coca-Cola Company, with numerous brands sold across more than 200 countries and territories, serves 2.2 billion drinks per day. We own 32 billion-dollar brands across several beverage categories worldwide. Our global portfolio of beverage brands includes the following:

• Sparkling Soft Drinks: Coca-Cola, Diet Coke/Coca-Cola Light, Coca-Cola Zero Sugar, Fanta, Fresca, Schweppes (owned by The Coca-Cola Company in certain countries other than the United States), Sprite and Thums Up
• Water, Sports, Coffee and Tea: Aquarius, Ayataka, BODYARMOR, Ciel, Costa, Crystal, Dasani, Fuze Tea, Georgia, glacéau smartwater, glacéau vitaminwater, Gold Peak, I LOHAS, Powerade and Topo Chico
• Juice, Value-Added Dairy and Plant-Based Beverages: Core Power, Del Valle, fairlife, innocent, Maaza, Minute Maid, Minute Maid Pulpy, Santa Clara and Simply

Our strong and stable bottling and distribution system helps us capture growth by manufacturing, distributing and selling existing, enhanced and new innovative products to consumers throughout the world.

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